Abstract
Named entities in text are persons, places, companies, etc. that are explicitly mentioned in text using proper nouns. The process of finding named entities in a text and classifying them to a semantic type, is called named entity recognition. Resolution of named entities is the process of linking a mention of a name in text to a pre-existing database entry. This grounds the mention in something analogous to a real world entity. For example, a mention of a judge named Mary Smith might be resolved to a database entry for a specific judge of a specific district of a specific state. This recognition and resolution of named entities can be leveraged in a number of ways including providing hypertext links to information stored about a particular judge: their education, who appointed them, their other case opinions, etc.
This paper discusses named entity recognition and resolution in legal documents such as US case law, depositions, and pleadings and other trial documents. The types of entities include judges, attorneys, companies, jurisdictions, and courts.
We outline three methods for named entity recognition, lookup, context rules, and statistical models. We then describe an actual system for finding named entities in legal text and evaluate its accuracy. Similarly, for resolution, we discuss our blocking techniques, our resolution features, and the supervised and semi-supervised machine learning techniques we employ for the final matching.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dozier, C., Haschart, R.: Automatic Extraction and Linking of Person Names in Legal Text. In: Proceedings of RIAO 2000, Recherche d’Information Assistee par Ordinateur, Paris, France, April 12-14, pp. 1305–1321 (2000)
Dozier, C., Zielund, T.: Cross Document Co-Reference Resolution Applications for People in the Legal Domain. In: Proceedings of the ACL 2004 Workshop on Reference Resolution and its Applications, Barcelona, Spain, July 25-26, pp. 9–16 (2004)
Chaudhary, M., Dozier, C., Atkinson, G., Berosik, G., Guo, X., Samler, S.: Mining Legal Text to Create a Litigation History Database. In: Proceedings of IASTED International Conference on Law and Technology, Cambridge, MA, USA (2006)
Quaresma, P., Gonçalves, T.: Using Linguistic Information and Machine Learning Techniques to Identify Entities from Juridical Documents. In: Francesconi, E., Montemagni, S., Peters, W., Tiscornia, D. (eds.) Semantic Processing of Legal Texts. LNCS (LNAI), vol. 6036, pp. 44–59. Springer, Heidelberg (2010)
Cohen, W., Ravikumar, P., Fienberg, S.: A Comparison of String Distance Metrics for Name-matching Tasks. In: Proc. II Web Workshop IJCAI, pp. 73–78 (2003)
Dozier, C., Veeramachaneni, S.: Names, Fame, and Co-Reference Resolution, Thomson Reuters Research and Development. Technical Report (2009)
Liao, W., Light, M., Veeramachaneni, S.: Integrating High Precision Rules with Statistical Sequence Classifiers for Accuracy and Speed. In: Proceedings of the NAACL 2009 Workshop Software engineering, testing, and quality assurance for Natural Language Processing (2009)
Yeh, A., Morgan, A., Colosimo, M., Hirschman, L.: BioCreative task 1A: Gene mention finding evaluation. BMC Bioinformatics 6(Suppl. 1) (2005)
Grishman, R., Sundheim, B.: Message Understanding Conference - 6: A Brief History. In: Proceedings of the 16th International Conference on Computational Linguistics (COLING), I, Kopenhagen (1996)
Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proc. of ICML (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Dozier, C., Kondadadi, R., Light, M., Vachher, A., Veeramachaneni, S., Wudali, R. (2010). Named Entity Recognition and Resolution in Legal Text. In: Francesconi, E., Montemagni, S., Peters, W., Tiscornia, D. (eds) Semantic Processing of Legal Texts. Lecture Notes in Computer Science(), vol 6036. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12837-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-12837-0_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12836-3
Online ISBN: 978-3-642-12837-0
eBook Packages: Computer ScienceComputer Science (R0)